Bibliographic record
Abstract
On March 22, 2021 the US, UK, Canada and the European Union imposed a coordinated series of sanctions on current and former Chinese officials, increasing pressure on China for alleged abuses in Xinjiang. China replied with its own sanctions on European officials. Indeed, very recently after this event, on June 10, 2021, China passed a new law to ‘‘counter foreign actions’’. It has rejected the allegations of abuse, stating that the camps are ‘‘re-education’’ facilities utilised to combat terrorism. According to Xinhua Net netizens expressed support for local brands after H&M and Nike came under fire in China for refusing to use Xinjiang cotton. In addition to this, 11 topics connected to Xinjiang cotton were on the trending list in China’s Twitter-like social media platform Sina Weibo, each issue attracting tens of millions of views and discussions. The study tried to relate this phenomenon to the concept of nationalism by taking literature from nationalism and media/media censorship theories, which mainly serve as a source of legitimacy and instrumental strategy for the state. The study aims to analyse the highest government’s management of nationalism and public opinion, during the time it faces international pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".